For our baseline architecture we use the resnet model which has been adapted to operate on 3D input. We follow the architecture proposed in [1]}.
However, we slightly tweak the ResNet architectures in [1]} for our purposes.
For instance, we do not use the BatchNorm layers in the ResNet architectures since we empirically observed that they showed much worse performance. Additionally, in contrast to [1]} for the first convolution layer we use the same stride for all three input channels. Besides these changes, we follow the same architecture as [1]}. For other specific architecture details please look at Table 1 in [1]}. The output of the ResNet model is forwarded through 2 fully-connected layers with outputs of size 64 and 1 respectively.
